An important question for climate change science is possible shifts in the extremes of regional water cycle, especially changes in patterns, intensity and/or frequency of extreme precipitation events. In this study, an analogue method is developed to help detect extreme precipitation events and their potential changes under future climate regimes without relying on the highly uncertain modeled precipitation. Our approach is based on the use of composite maps to identify the distinct synoptic and large-scale atmospheric conditions that lead to extreme precipitation events at local scales. The analysis of extreme daily precipitation events, exemplified in the south-central United States, is carried out using 62-yr (1948-2010) CPC gridded station data and NASA’s Modern Era Retrospective-analysis for Research and Applications (MERRA). Various aspects of the daily extremes are examined, including their historical ranking, associated common circulation features at upper and lower levels of the atmosphere, and moisture plumes. The scheme is first evaluated for the multiple climate model simulations of the 20th century from Coupled Model Intercomparison Project Phase 5 (CMIP5) archive to determine whether the statistical nature of modeled precipitation events (i.e. the numbers of occurrences over each season) could well correspond to that of the observed. Further, the approach will be applied to the CMIP5 multi-model projections of various climate change scenarios (i.e. Representative Concentration Pathways (RCP) scenarios) in the next century to assess the potential changes in the probability of extreme precipitation events. The research results from this study should be of particular significance to help society develop adaptive strategies and prevent catastrophic losses.